Power Flow Analysis on CUDA-based GPU

This major qualifying project investigates the algorithm and the performance of using the CUDA-based Graphics Processing Unit for power flow analysis. The accomplished work includes the design, implementation and testing of the power flow solver. Comprehensive analysis shows that the execution time of the parallel algorithm outperforms that of the sequential algorithm by several factors

Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee

Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only readily available voltage magnitudes, eliminating the need for costly phase angles or power flow data. Given the sensor data, many existing detection methods based on change-point detection require prior knowledge of outage patterns, which are unknown for real-world outage scenarios. To remove this impractical requirement, we propose a data-driven method to learn the parameters of the post-outage distribution through gradient descent. However, directly using gradient descent presents feasibility issues. To address this, we modify our approach by adding a Bregman divergence constraint to control the trajectory of the parameter updates, which eliminates the feasibility problems. As timely operation is the key nowadays, we prove that the optimal parameters can be learned with convergence guarantees via leveraging the statistical and physical properties of voltage data. We evaluate our approach using many representative distribution grids and real load profiles with 17 outage configurations. The results show that we can detect and localize the outage in a timely manner with only voltage magnitudes and without assuming a prior knowledge of outage patterns.Comment: 12 page

Distribution Grid Line Outage Detection with Privacy Data

Change point detection is important for many real-world applications. While sensor readings enable line outage identification, they bring privacy concerns by allowing an adversary to divulge sensitive information such as household occupancy and economic status. In this paper, to preserve privacy, we develop a decentralized randomizing scheme to ensure no direct exposure of each user's raw data. Brought by the randomizing scheme, the trade-off between privacy gain and degradation of change point detection performance is quantified via studying the differential privacy framework and the Kullback-Leibler divergence. Furthermore, we propose a novel statistic to mitigate the impact of randomness, making our detection procedure both privacy-preserving and have optimal performance. The results of comprehensive experiments show that our proposed framework can effectively find the outage with privacy guarantees.Comment: 5 page

The Impacts of the MA Health Care Reform on Hospital Costs and Quality

In 2006, the Massachusetts legislature passed the health care reform bill. The main targets of this reform were to contain hospital costs, to increase insurance rate and to improve quality of medical services. The primary focus of this paper is to explore the overall impact of the Massachusetts Health Care Reform on hospital operational costs and quality of medical services. Three econometric models of cost, salaries and quality were developed to examine the impacts of the reform on the hospital cost and quality. Consistent with our predictions, the final regression results show that the reform had positive effects on the quality of Massachusetts hospital services and the costs of Massachusetts hospitals were contained